Electronic applications and digital services have continuously expanded over the last decade where both consumer and corporate markets have driven the adoption of mobile/wireless communication handheld devices, such as Android®, iOS®, BlackBerry® smartphones and tablets. However, despite that all of these services provide great values to end-users, there is a growing concern about security and more particularly about identity theft.
More precisely, various applications and services rely on prompting the end-user to enter a user identification and password for authentication purposes. In regards of the many hacking techniques that have been developed, it is now fairly understood the user identification and password paradigm is not secure anymore.
As a consequence, there is a huge demand in the market to find out a secure way to protect digital transactions, services or any kind of digital accesses where data must be kept confidential. Logically, the data security industry has put an important effort to develop biometric solutions, including fingerprint, iris or face scan. In the current art, biometric techniques are still very expensive and difficult to roll-out at a large scale, mainly due to the fact they require specific installations and/or scan procedures. As an example, there are many existing 3D scan solutions using fixed cameras used to reconstitute a 3D model.
One biometric technology involves a visual scan of the individual's attribute, such as the face, by using the individual's wireless handheld device. In such existing technology, 3D object reconstruction for mobile devices is based on detecting features of the biometric attribute and tracking them. In an initialization phase, the camera positions are estimated. Following frames are captured and are added in an iterative way and the camera is better estimated while computing in parallel depth maps (i.e., information relating to the distance of the surfaces of scene objects from a viewpoint) for individual pairs of frames. As a final step, all depth maps can be combined by depth map fusion algorithms to reconstruct the object in 3D.
Currently, these 3D object reconstruction approaches provide acceptable results to reconstruct 3D object with good accuracy in certain application. For example, the level of quality provided by existing technologies may be acceptable of software applications such as the reconstruction of 3D facial avatars or printing out of 3D figurines. However, when requiring higher accuracy (e.g., to protect confidential electronic data), the method requires a significant number of iterations and key frames to reconstruct accurate 3D models. Moreover, the quality of the data and its correspondence is still unknown.